Travelling the world of gene-gene interactions

Briefings in Bioinformatics

Volumn 13, Issue 1, 2012, Pages 1-19

  Van steen, Kristel ^a  

^a Grande Traverse ^*  (Belgium)

Author keywords

Controlling false positives; Gene gene interaction; Translational medicine; Variable selection

Indexed keywords

ANIMAL; ARTICLE; BIOLOGICAL MODEL; GENE; GENETIC EPISTASIS; GENOTYPE; HUMAN;

ANIMALS; EPISTASIS, GENETIC; GENES; GENOTYPE; HUMANS; MODELS, GENETIC;

EID: 84855661466     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr012     Document Type: Article

References (190)

1. Carlborg O, Haley CS. Epistasis: too often neglected in complex trait studies? Nat Rev Genet 2004;5:618-4.
2. Joyce AR, Palsson BO. The model organism as a system: integrating 'omics' data sets. Nat RevMol Cell Biol 2006;7: 198-210.
3. Sanjuan R, Elena SF. Epistasis correlates to genomic complexity. ProcNatl Acad SciUSA 2006;103:14402-5.
4. Sanjuan R, Nebot MR. A network model for the correlation between epistasis and genomic complexity. PLoS ONE 2008;3:e2663.
5. Moore JH. A global view of epistasis. Nat Genet 2005;37: 13-4.
6. Emily M, Mailund T, Hein J, et al. Using biological networks to search for interacting loci in genome-wide association studies. EurJ Hum Genet 2009;17:1231-40.
7. Wu J, Devlin B, Ringquist S, et al. Screen and clean: a tool for identifying interactions in genome-wide association studies. Genet Epidemiol 2010;34:275-85.
8. Bateson W, Mendel G. Mendel's Principles of Heredity. A Defence, with aTranslation ofMendel'sOriginal Papers onHybridisation. Cambridge: Cambridge University Press, 1902.
9. Fisher R. The correlation between relatives on the supposition of Mendelian inheritance. TransRSoc Edinb 1918; 52:399-433.
10. Wang X, Elston RC, Zhu X. Statistical interaction in human genetics: how should we model it if we are looking for biological interaction? Nat RevGenet 2010;12:74.
11. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nat RevGenet 2009;10:392-404.
12. Wang X, Elston RC, Zhu X. The meaning of interaction. HumHered 2010;70:269-77.
13. Phillips PC. Epistasis - the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 2008;9:855-67.
14. Gregersen JW, Kranc KR, Ke X, et al. Functional epistasis on a common MHC haplotype associated with multiple sclerosis. Nature 2006;443:574-7.
15. Davierwala AP, Haynes J, Li Z, et al. The synthetic genetic interaction spectrum of essential genes. Nat Genet 2005;37: 1147-52.
16. Culverhouse R, Suarez BK, Lin J, et al. A perspective on epistasis: limits of models displaying no main effect. Am JHum Genet 2002;70:461-71.
17. Li W, Reich J. A complete enumeration and classification of two-locus disease models. Hum Hered 2000;50: 334-49.
18. Hallgrimsdottir IB, Yuster DS. A complete classification of epistatic two-locus models. BMCGenet 2008;9:17.
19. Onkamo P, Toivonen H. A survey of data mining methods for linkage disequilibrium mapping. HumGenom 2006; 2:336-40.
20. Musani SK, Shriner D, Liu NJ, et al. Detection of gene x gene interactions in genome-wide association studies of human population data. HumHered 2007;63:67-84.
21. Moore JH. Mining patterns of epistasis in human genetics. In: Chen JY, Lonardi S, (eds). BiologicalDataMining. New York: Chapman and Hall, 2009.
22. Guyon I, Gunn S, Nikravesh M, et al. Feature extraction, foundations and applications. In: Guyon I, Gunn S, Nikravesh M, Zadeh L, (eds). Series Studies in Fuzziness and Soft Computing. Physica-Verlag: Springer, 2006.
23. Li J, Tang R, Biernacka JM, et al. Identification of gene-gene interaction using principal components. BMC Proc 2009;3(Suppl 7):S78.
24. Guyon I, Elisseeff A. An introduction to variable selection and feature selection. J Machine Learn Res 2003;3: 1157-82.
25. Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007;23: 2507-17.
26. Dong CZ, Chu X, Wang Y, et al. Exploration of gene-gene interaction effects using entropy-based methods. EurJ Hum Genet 2008;16:229-35.
27. Varadan V, Miller DM 3rd, Anastassiou D. Computational inference of the molecular logic for synaptic connectivity in C. elegans. Bioinformatics 2006;22:e497-506.
28. Kononenko I. Estimation attributes: analysis and extensions of RELIEF. Berlin, Heidelberg: Springer, 1994.
29. Moore JH, White BC. Tuning reliefF for genome-wide genetic analysis. In: Marchiori E, Moore JH, Rajapakse JC, (eds). EvoBIO 2007, LNCS 4447. Berlin, Heidelberg: Springer-Verlag, 2007;166-75.
30. Greene CS, Penrod NM, Kiralis J, et al. Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions. BioDataMin 2009;2:5.
31. McKinney BA, Reif DM, White BC, et al. Evaporative cooling feature selection for genotypic data involving interactions. Bioinformatics 2007;23:2113-20.
32. Nunkesser R, Bernholt T, Schwender H, et al. Detecting high-order interactions of single nucleotide polymorphisms using genetic programming. Bioinformatics 2007;23: 3280-8.
33. Greene CS, White BC, Moore JH. Ant colony optimization for genome-wide genetic analysis. Lect Notes Comput Sci 2008;5217/2008:37-47.
34. Wang Y, Liu X, Robbins K, etal. AntiEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm. BMC Res Notes 2010;3:117.
35. Schwender H, Ickstadt K. Identification of SNP interactions using logic regression. Biostatistics 2008;9: 187-98.
36. Schwender H, Ruczinski I, Ickstadt K. Testing SNPs and sets of SNPs for importance in association studies. Biostatistics 2011;12:18-32.
37. Anastassiou D. Computational analysis of the synergy among multiple interacting genes. Mol Syst Biol 2007;3:83.
38. Chanda P, Sucheston L, Zhang A, et al. AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics 2008;180:1191-210.
39. Chanda P, Sucheston L, Zhang AD, et al. The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors. EurJ Hum Genet 2009;17:1274-86.
40. Chanda P, Zhang A, Brazeau D, et al. Informationtheoretic metrics for visualizing gene-environment interactions. AmJHum Genet 2007;81:939-63.
41. Shannon CE. A mathematical theory of communication. Bell SystTechJ 1948;27:379-423, 623-56.
42. Jakulin A. Attribute interactions in machine learning. Faculty of Computer and Information Science.. University of Ljubljana, 2003.
43. van der Linde A, Tutz G. On association in regression: the coefficient of determination revisited. Stat Med 2008;42: 1-24.
44. Kullback S. InformationTheory and Statistics. Mineola, New York: Dover Publisher, 1968.
45. Sucheston L, Chanda P, Zhang A, et al. Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity. BMC Genomics 2010;3:487.
46. Kononenko I, Robnik-Sikonia M, Pompe U. ReliefF for estimation and discretization of attributes in classification, regression and IPL problems. In: Ramsay A, (ed). Artificial Intelligence: Methodology, Systems, Applications: Proceedings of AIMSA'96. Amsterdam, the Netherlands: IOS Press, 1996;31-40.
47. Marchini J, Donnelly P, Cardon LR. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 2005;37:413-7.
48. Calle ML, Urrea V, Vellalta G, et al. Improving strategies for detecting genetic patterns of disease susceptibility in association studies. Stat in Med 2008;27: 632-6546.
49. Moore JH. Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. In: Zhu X, Davidson I, (eds). Knowledge Discovery and Data Mining: Challenges and Realities with RealWorld Data. Hershey: IGI Press, 2007;17-30.
50. De Lobel L, Geurts P, Baele G, et al. A screening methodology based on Random Forests to improve the detection of gene-gene interactions. Eur J Hum Genet 2010;18: 1127-32.
51. Schwarz DF, Konig IR, Ziegler A. On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data. Bioinformatics 2010;26: 1752-8.
52. Yang C, He Z, Wan X, et al. SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies. Bioinformatics 2009;25: 504-11.
53. Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007;39: 1167-73.
54. Dorigo M, Stützle T. AntColonyOptimization. MIT Press, 2004. ISBN 0-262-04219-3.
55. Tyron RC. Cluster Analysis. New-York: McGraw-Hill, 1939.
56. Breiman L, Friedman JH, Olshen RA, et al. Classification and RegressionTrees.. Taylor & Francis Inc., 1984.
57. Ripley BD. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press, 1996.
58. Mechanic LE, Luke BT, Goodman JE, etal. Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions. BMC Bioinformatics 2008;9: 146.
59. Ritchie MD, Hahn LW, Roodi N, et al. Multifactordimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. AmJHum Genet 2001;69:138-47.
60. Nelson MR, Kardia SL, Sing CF. The combinatorial partitioning method. Combin Pattern Match 2000;1848: 293-304.
61. Culverhouse R. The use of the restricted partition method with case-control data. HumHered 2007;63:93-100.
62. Culverhouse R, Jin W, Jin CH, et al. Power and false-positive rates for the restricted partition method (RPM) in a large candidate gene data set. BMC Proc 2009;3(Suppl 7):S74.
63. Sun X, Zhang Z, Zhang Y, et al. Multi-locus penetrance variance analysis method for association study in complex diseases. HumHered 2005;60:143-9.
64. Kooperberg C, Ruczinski I. Identifying interacting SNPs using Monte Carlo logic regression. Genet Epidemiol 2005; 28:157-70.
65. Ruczinski I, Kooperberg C, LeBlanc ML. Logic regression. JComputGraph Stat 2003;12:475-511.
66. Zheng T, Wang H, Lo SH. Backward genotype-trait association (BGTA)-based dissection of complex traits in case-control designs. HumHered 2006;62:196-212.
67. Yang P, Ho JW, Zomaya AY, et al. A genetic ensemble approach for gene-gene interaction identification. BMC Bioinformatics 2010;11:524.
68. Wolf BJ, Hill EG, Slate EH. Logic forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics 2010;26:2183-9.
69. Turner SD, Dudek SM, Ritchie MD. Grammatical evolution of neural networks for discovering epistasis among quantitative trait loci. In: Pizzuti C, Ritchie MD, Giacobini M, (eds). EvoBIO 2010, LNCS 6023. Berlin, Heidelberg: Springer-Verlag, 2010;86-97.
70. Bureau A, Dupuis J, Falls K, et al. Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol 2005;28:171-82.
71. Jiang R, Tang WW, Wu XB, et al. A random forest approach to the detection of epistatic interactions in case-control studies. BMC Bioinformatics 2009; 10(Suppl 1):S65.
72. Lunetta KL, Hayward LB, Segal J, et al. Screening large-scale association study data: exploiting interactions using random forests. BMCGenet 2004;5:32.
73. Prinzie A, Van den Poel D. Random forests for multiclass classification: random multinomial logit, expert systems with applications. Expert Syst Appl 2008;34:1721-32.
74. Prinzie A, Van den Poel D. Random multiclass classification: generalizing random forests to random MNL and random NB. Database Expert SystAppl 2007;349-58.
75. Karpievitch YV, Hill EG, Leclerc AP, et al. An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF{thorn}{thorn}. PLoSONE 2009;4:e7087.
76. Sela RJ, Simonoff JS. Re-Em rees: a new data mining approach for longitudinal data. Statistics Working Papers Series 2009.
77. Calle ML, Urrea V, Malats N, et al. mbmdr: an R package for exploring gene-gene interactions associated with binary or quantitative traits. Bioinformatics 2010;26: 2198-9.
78. Calle ML, Urrea V, Malats N, et al. Model-BasedMultifactor Dimensionality Reduction for detecting interactions in high-dimensional genomic data. Technical Report n. 24. Department of Systems Biology, Universitat de Vic, 2008.
79. Kim Y, Wojciechowski R, Sung H, et al. Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects. BMC Proc 2009;3(Suppl 7):S64.
80. Wongseree W, Assawamakin A, Piroonratana T, et al. Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses. Bmc Bioinformatics 2009;10:294.
81. Mahachie John JM, Cattaert T, Van Lishout F, et al. Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data. EurJ Hum Genet 2011;1-8.
82. Carlborg O, Andersson L, Kinghorn B. The use of a genetic algorithm for simultaneous mapping of multiple interacting quantitative trait loci. Genetics 2000;155: 2003-10.
83. Bellman R, Kalaba R. A mathematical theory of adaptive control processes. Proc Natl Acad Sci USA 1959;45: 1288-90.
84. Chapman J, Clayton D. Detecting association using epistatic information. Genet Epidemiol 2007;31:894-909.
85. Chatterjee N, Kalaylioglu Z, Moslehi R, et al. Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions. AmJ Hum Genet 2006;79:1002-16.
86. VanderWeele TJ. Empirical tests for compositional epistasis. Nat Rev Genet 2010;11:166.
87. VanderWeele TJ. Epistatic interactions. Stat Appl Genet Mol Biol 2010;9:Article 1.
88. VanderWeele TJ, Laird NM. Tests for compositional epistasis under single interaction-parameter models. Ann Hum Genet 2010;75:146-56.
89. Gao H, Granka JM, Feldman MW. On the classification of epistatic interactions. Genetics 2010;184:827-37.
90. Ionita I, Man M. Optimal two-stage strategy for detecting interacting genes in complex diseases. BMCGenet 2006;7: 39.
91. North BV, Curtis D, Sham PC. Application of logistic regression to case-control association studies involving two causative loci. HumHered 2005;59:79-87.
92. Park M, Hastie T. Penalized logistic regression for detecting gene-gene interactions. Biostatistics 2007;9: 30-50.
93. Tanck MW, Jukema JW, Zwinderman AH. Simultaneous estimation of gene-gene and gene-environment interactions for numerous loci using double penalized log-likelihood. Genet Epidemiol 2006;30:645-51.
94. Lin HY, Wang W, Liu YH, et al. Comparison of multivariate adaptive regression splines and logistic regression in detecting SNP-SNP interactions and their application in prostate cancer. J Hum Genet 2008;53: 802-11.
95. Huang J, Breheny P, Ma S, et al. The Mnet Method for Variable Selection.. Department of Statistics and Actuarial Science, The University of Iowa, 2010.
96. Wang T, Ho G, Ye K, etal. A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped. Genet Epidemiol 2009;33:6-15.
97. Wan X, Yang C, Yang Q, etal. BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. AmJHum Genet 2010;87:325-40.
98. Yang C, Wan X, Yang Q, et al. Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group Lasso. BMC Bioinformatics 2010; 11(Suppl 1):S18.
99. Wang Z, Liu T, Lin Z, et al. A general model for multilocus epistatic interactions in case-control studies. PLoS ONE 2010;5:e11384.
100. Viallefont V, Raftery AE, Richardson S. Variable selection and Bayesian model averaging in case-control studies. Stat Med 2001;20:3215-30.
101. Butte AS, Kohane IS. Relevance networks: a first step towards finding genetic regulatory networks within microarray data. In: Parmigiani G, Garrett ES, Irizarry RA, et al, (eds). The Analysis of Gene Expression Data. New York: Springer, 2003;428-46.
102. Schafer J, Strimmer K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 2005;21:754-64.
103. Andrei A, Kendziorski C. An efficient method for identifying statistical interactors in gene association networks. Biostatistics 2009;10:706-18.
104. Chu JH, Weiss ST, Carey VJ, et al. A graphical model approach for inferring large-scale networks integrating gene expression and genetic polymorphism. BMC Syst Biol 2009;3:55.
105. Zwick M. Reconstructability analysis of epistasis. AnnHum Genet 2011;75:157-71.
106. Kam-Thong T, Czamara D, Tsuda K, et al. EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units. Eur J Hum Genet, doi:10.1038/ejhg.2010.196 [Epub ahead of print 8 December 2010].
107. Ruczinski I, Kooperberg C, LeBlanc ML. Logic regressionmethods and software. In: Denison D, HansenM, Holmes C, et al, (eds). MSRI Workshop on Nonlinear Estimation and Classification. New York: Springer, 2002;333-44.
108. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461: 747-53.
109. Cordell HJ. Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 2002;11:2463-8.
110. De Lobel L, De Meyer H, Thijs L, et al. A family-based association test to detect gene-gene interactions in the presence of linkage. Genet Epidemiol 2009;33:771-2.
111. Cattaert T, Urrea V, Naj AC, et al. FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLoS ONE 2010;5:e10304.
112. Aulchenko YS, de Koning DJ, Haley C. Genomewide rapid association using mixed model and regression: A fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 2007; 177:577-85.
113. Purcell S, Neale B, Todd-Brown K, et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. AmJHum Genet 2007;81:559-75.
114. Sinnott-Armstrong NA, Greene CS, Cancare F, et al. Accelerating epistasis analysis in human genetics with consumer graphics hardware. BMC Res Notes 2009;2: 149.
115. Schupbach T, Xenarios I, Bergmann S, et al. FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics 2010;26:1468-9.
116. Wang Z, Wang Y, Tan K-L, et al. CEO: a cloud epistasis computing model in GWAS. International Conference on Bioinformatics & Biomedicine, Hong Kong, 2010.
117. Sinnott-Armstrong NA, Greene CS, Moore JH. Fast genome-wide epistasis analysis using ant colony optimization for multifactor dimensionality reduction analysis on graphics processing units. GECCO 2010;215-6.
118. Zhang XA, Pan F, Xie YY, et al. COE: a general approach for efficient genome-wide two-locus epistasis test in disease association study. JComputBiol 2010;17:401-15.
119. Zhang X, Huang SP, Zou F, et al. TEAM: efficient two-locus epistasis tests in human genome-wide association study. Bioinformatics 2010;26:i217-27.
120. Hochberg Y, Tamhane AC. MultipleComparison Procedures. New York: Wiley, 1987.
121. Dudbridge F, Gusnanto A, Koeleman BP. Detecting multiple associations in genome-wide studies. Hum Genomics 2006;2:310-7.
122. Dudoit S, Van der Laan MJ. Multiple testing procedures with applications to genomics. New York: Springer, 2008; XXXIII, 588. s p.
123. Manly KF, Nettleton D, Hwang JT. Genomics, prior probability, and statistical tests of multiple hypotheses. Genome Res 2004;14:997-1001.
124. Pollard KS, van der Laan MJ. Choice of a null distribution in resampling-based multiple testing. J Stat Plann Inference 2004;125:85-100.
125. Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. AmJHum Genet 2004;74:765-9.
126. Moskvina V, Schmidt KM. On multiple-testing correction in genome-wide association studies. Genet Epidemiol 2008; 32:567-73.
127. Nicodemus KK, Liu WL, Chase GA, et al. Comparison of type I error for multiple test corrections in large single-nucleotide polymorphism studies using principal components versus haplotype blocking algorithms. BMC Genet 2005;6(Suppl 1):S78.
128. Sun W, Cai T. Large-scale multiple testing under dependence. JRStat Soc B 2009;71:393-424.
129. Wei Z, Sun W, Wang K, et al. Multiple testing in genome-wide association studies via hidden Markov models. Bioinformatics 2009;25:2802-8.
130. Kall L, Storey JD, Noble WS. QVALITY: non-parametric estimation of q-values and posterior error probabilities. Bioinformatics 2009;25:964-6.
131. Strimmer K. fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 2008;24:1461-2.
132. Storey JD. A direct approach to false discovery rates. J R Stat Soc B 2002;64:479-98.
133. Storey JD. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat 2003;31: 2013-35.
134. Wacholder S, Chanock S, Garcia-Closas M, et al. Assessing the probability that a positive report is false: An approach for molecular epidemiology studies - response. J Natl Cancer Inst 2004;96:1722-3.
135. Wacholder S, Chanock S, Garcia-Closas M, et al. Assessing the probability that a positive report is false: An approach for molecular epidemiology studies. JNatlCancer Inst 2004; 96:434-42.
136. Efron B, Tibshirani R, Storey JD, et al. Empirical Bayes analysis of a microarray experiment. JAm StatAssoc 2001; 96:1151-60.
137. Westfall PH, Young SS. Resampling-Based MultipleTesting: Examples and Methods for p-value Adjustment. New-York: Wiley, 1993.
138. Mahachie John JM, Cattaert T, De Lobel L, et al. Comparison of genetic association strategies in the presence of rare alleles 2011.
139. Nacu S, Critchley-Thorne R, Lee P, etal. Gene expression network analysis and applications to immunology. Bioinformatics 2007;23:850-8.
140. Hothorn LA, Hothorn T. Order-restricted scores test for the evaluation of population-based case-control studies when the genetic model is unknown. Biometrical J 2009; 51:659-69.
141. de Bakker P, Ferreira M, Jia X, et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. HumanMol Genet 2008;17:R122.
142. Good P. Introduction to StatisticsThrough ResamplingMethods andR/S-PLUS. New York: Wiley, 2005.
143. Dering C, Pugh E, Ziegler A. Statistical analysis of rare sequence variants: an overview of collapsing methods. BMC Proc 2011.
144. Nettleton D, Doerge RW. Accounting for variability in the use of permutation testing to detect quantitative trait loci. Biometrics 2000;56:52-8.
145. Churchill GA, Doerge RW. Empirical threshold values for quantitative trait mapping. Genetics 1994;138:963-71.
146. Doerge RW, Churchill GA. Permutation tests for multiple loci affecting a quantitative character. Genetics 1996;142: 285-94.
147. Carlborg R, Andersson L. Use of randomization testing to detect multiple epistatic QTLs. Genet Res Camb 2002;79: 175-84.
148. Dudbridge F, Koeleman BP. Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. AmJHum Genet 2004;75:424-35.
149. Knijnenburg TA, Wessels LF, Reinders MJ, et al. Fewer permutations, more accurate P-values. Bioinformatics 2009; 25:i161-8.
150. Pattin KA, White BC, Barney N, et al. A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Genet Epidemiol 2009;33:87-94.
151. Han B, Kang HM, Eskin E. Rapid and accurate multiple testing correction and power estimation for millions of correlated markers. PLoS Genet 2009;5:e1000456.
152. Conneely KN, Boehnke M. So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests. AmJHum Genet 2007;81:1158-68.
153. Yao X. Evolving artificial neural networks. IEEE 1999;87: 1423-47.
154. Turner SD, Dudek SM, Ritchie MD. ATHENA: a knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait loci. BioDataMin 2010;3:5.
155. Kooperberg C, LeBlanc M. Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genetic Epidemiol 2008;32:255-63.
156. Pe'er I, de Bakker PIW, Maller J, et al. Evaluating and improving power in whole-genome association studies using fixed marker sets. Nat Genet 2006;38:663-7.
157. Peng QQ, Zhao JH, Xue FZ. A gene-based method for detecting gene-gene co-association in a case-control association study. EurJ Hum Genet 2010;18:582-7.
158. Zhang J, Li J, Deng HW. Identifying gene interaction enrichment for gene expression data. PLoS ONE 2009;4: e8064.
159. Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac Symp Biocomput 2009;14:368-79.
160. Haig D. Does heritability hide in epistasis between linked SNPs? EurJ Hum Genet 2010;19:123.
161. Moore JH, Gilbert JC, Tsai CT, et al. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 2006;241: 252-61.
162. Zhao JY, Jin L, Xiong MM. Test for interaction between two unlinked loci. AmJHuman Genet 2006;79:831-45.
163. Cockerham CC. An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 1954; 39:859-82.
164. Cordell HJ, Todd JA, Bennett ST, et al. Two-locus maximum lod score analysis of a multifactorial trait: joint consideration of IDDM2 and IDDM4 with IDDM1 in type 1 diabetes. AmJHum Genet 1995;57:920-34.
165. Farrall M. Reports of the death of the epistasis model are greatly exaggerated. Am J Hum Genet 2003;73:1467-8; author reply 1471-63.
166. Zhang YL, Ji L. Main-effects model is a special kind of additive model in the presence of linkage disequilibrium. HumHered 2009;67:13-25.
167. Robinson R. Common disease, multiple rare (and distant) variants. PLoS Biol 2010;8:e1000293.
168. Eichler EE, Flint J, Gibson G, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 2010;11:446-50.
169. Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 2008;40:695-701.
170. Dickson SP, Wang K, Krantz I, et al. Rare variants create synthetic genome-wide associations. PLoS Biol 2010;8: e1000294.
171. Sharma A, Chavali S, Mahajan A, et al. Genetic association, post-translational modification, and protein-protein interactions in type 2 diabetes mellitus. MolCellProteomics 2005; 4:1029-37.
172. Bansal V, Libiger O, Torkamani A, et al. Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet 11:773-85.
173. Liu DJ, Leal SM. A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet 2010;6: e1001156.
174. Madsen BE, Browning SR. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 2009;5:e1000384.
175. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. AmJHum Genet 2008;83:311-21.
176. Franke L, Jansen RC. eQTL analysis in humans. Methods Mol Biol 2009;573:311-28.
177. Ritchie MD. Using prior knowledge and genome-wide association to identify pathways involved in multiple sclerosis. GenomeMed 2009;1:65.
178. Thomas DC. The need for a systematic approach to complex pathways in molecular epidemiology. CancerEpidemiol Biomarkers Prev 2005;14:557-9.
179. Pattin KA, Moore JH. Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases. Human Genet 2008;124:19-29.
180. Baranzini SE, Galwey NW, Wang J, et al. Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 2009;18: 2078-90.
181. Lee I, Lehner B, Vavouri T, etal. Predicting genetic modifier loci using functional gene networks. GenomeRes 2010; 20:1143-53.
182. Kelley R, Ideker T. Systematic interpretation of genetic interactions using protein networks. Nature Biotechnol 2005; 23:561-6.
183. Lin A, Wang RT, Ahn S, et al. A genome-wide map of human genetic interactions inferred from radiation hybrid genotypes. Genome Res 2010;20:1122-32.
184. Moore JH, Williams SM. Epistasis and its implications for personal genetics. AmJHum Genet 2009;85:309-20.
185. d'Ocagne M. Sur les divers modes d'application de la méthode graphique a' l'art du calcul. Calcul graphique et calcul nomographique. In: Duporcq E, (ed). Deuxie'me Congre's International des Mathe{currency sign}maticiens. Paris: Gauthier-Villars, 1902, 419-24.
186. Lee KM, Kim WJ, Ryu KH, Lee SH. A nomogram construction method using genetic algorithm and naive Bayesian technique. Proceedings of the 11th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering 2009, November 07-09, 2009, p.145-49. Baltimore, MD, USA.
187. Gauderman WJ. Sample size requirements for association studies of gene-gene interaction. AmJ Epidemiol 2002;155: 478-84.
188. Vermeulen SH, Den Heijer M, Sham P, et al. Application of multi-locus analytical methods to identify interacting loci in case-control studies. Ann Hum Genet 2007;71: 689-700.
189. Milne RL, Fagerholm R, Nevanlinna H, etal. The importance of replication in gene-gene interaction studies: multifactor dimensionality reduction applied to a two-stage breast cancer case-control study. Carcinogenesis 2008;29: 1215-8.
190. Böhringer S, Hardt C, Miterski B, et al. Multilocus statistics to uncover epistasis and heterogeneity in complex diseases: revisiting a set of multiple sclerosis data. Eur J Hum Genet 2003;11:573-84.